Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a), first paragraph, as failing to comply with the written description requirement because the claims, as supported by the specification, functionally express desired outcomes obtained from the use of some generic “machine learning application” without providing any technical detail as to how the outcomes are achieved by machine learning. One skilled in the relevant art, reading the claims in light of the specification, would not be able to conclude that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
The step limitations reciting to “determine, by a machine learning application” and to “generate” and “perform…by the machine learning application” certain information (skill level, user profiles, and context-action mappings), see claims 1, 8 and 17, fail to meet the 35 U.S.C. 112(a) requirement for written description support. The specification merely expresses a desire to use any of a variety of known learning models to somehow obtain certain functional outcomes (determine golf player skill and profiles and output actions) without providing any technical details as to how machine learning achieves these outcomes.
MPEP 2163.03(v) cites Ariad Pharms., Inc. v. Eli Lilly & Co. as evidence that, “An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved” (emphasis added). This section also cites Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) as evidence that, “The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement."
Although the specification at first glance contains numerous references to machine learning, a closer look reveals that all of these references are drafted as usage scenarios amounting to desired fields of use of existing prior art machine learning techniques.
[0039] and [0057] of the specification, for example, contemplates that learning models 108 may comprise GAI, LLM’s, linear regression, RAG applications, deep learning, supervised or unsupervised learning, CNN’s, LSTM “or any other suitable artificial intelligence models, frameworks, or other applications configured to perform the various functionalities” of the invention. And [0082] describes that one or more of these learning models may “be stored on the vehicle control system” for certain fields of use (detecting objects, people, providing AI virtual assistants.)
Taking into consideration the level of ordinary skill in the art, the complexity of the art, and the criticality of the claimed features involving machine learning to the practice of the invention, it is determined that there is insufficient support in the specification to conclude that Applicant had possession of the invention including the claimed “determine,” “generate,” and “perform[ing]” certain functions by “a machine learning application”.
Mere mention of a desire to apply any known prior art learning model through unspecified programming instructions to accomplish a conceived-of end result is insufficient to prove the inventor had possession of the invention configured to accomplish the end result. The specification must particularly point out how functional outcomes such as detecting a user skill level using machine learning is done – such as a description of what particular inputs are provided to a learning model, what the learning model is and how it is structured, how the learning model is trained, how the learning model is programmed to make inferences, how inferences are used to generate particular outputs, how outputs are formatted or conveyed or used by the system, and what the architecture of the system is.
An example of what would be considered sufficient written description to claim the use of neural networks in a field of use (in this case audio processing) exists in US 10,068,557 B1 to Engel et al. The specification of Engl goes into detail as to how an encoder neural network receives input, what type of input it receives, how the encoder neural network is structured (a stack of non-causal convolutional layers), how the encoder neural network operates (embedding certain information such as pitch information, upsampling processes, etc.), how the encoder neural network is trained (on a particular loss function which can be backpropagated through a decoder neural network and continue through an encoder neural network).
This sort of detail stands in contrast to the instant disclosure that merely contemplates ideas of fields of use of various prior art learning models.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims are directed to recommending a golf club to a golfer based on observing how they play a round of golf. Observing game play, evaluating and judging what clubs would best suit a user in certain situations, and outputting a suggestion on what clubs to use are equivalent to human mental work that belongs to the enumerated abstract idea grouping of “Mental Processes.” And one person teaching another person how to improve their golf performance including through giving instructions is “Certain Methods of Organizing Human Activity”. The claimed activities are traditionally done by human beings through organized thought and interaction.
The claims functionally recite desired end results (“collect,” “determine”, “generate”, “receive”, “perform”) without any technical description of how any particular computers are programmed to accomplish these results. No nonobvious hardware, system architecture, or programming operations or combination thereof are claimed, only lists of ideas. And these ideas are squarely about teaching a golfer based on observed performance.
The claims do no recite any technical details of why or how machine learning is used to accomplish the stated results or provide any improvements solving any specific problems existing in the art. The claims lack any details as to how computers are programmed to receive input data or what type of data is received, what type of learning model is used, how any particular computers are programmed to train the model, what steps the model uses to make inferences about appropriate outputs, or how output data is generated. The claims are essentially a list of functionally-recited desired end results for applying generic, existing of machine learning language model training in a conceived field of use. Because the claims lack any description of novel hardware and/or software and because a machine learning model is merely claimed as a field of use, no improvements to the performance of computers or to the field of machine learning models is found.
Claims to training a prior art machine learning model with certain types of data have been held to be ineligible in the Court of Appeals for the Federal Circuit in the suits against Fox Corp. Fox Broadcasting Company, LLC and Fox Sports Productions, LLC. Here the court considered that when claims merely use machine learning to claim an abstract idea itself by “using a generic machine learning technique in a particular environment,” this is a failure to transform the claimed abstract idea into “significantly more” – there is “no inventive concept.” The CAFC opinion concluded with a note that, “[m]achine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology.” The court explained that its instant opinion held “only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
The pending claims are limited in scope to the application of generic machine learning to data associated with golfer performance, and similarly are found to be ineligible. The same conclusion is reached that there is no inventive concept. A detailed analysis that was conducted in reaching this conclusion follows.
Detailed Analysis
The following detailed analysis is based on the subject matter eligibility examination guidelines provided in the MPEP at https://www.uspto.gov/web/offices/pac/mpep/s2106.html
Steps 1 and 2 of the analysis have been conducted for all of the pending claims.
Step 1 (See MPEP §2106.03): In this step, it is determined whether the pending claims are directed to at least one of the four statutory categories of subject matter. Here it is determined that all of the pending claims fall into statutory categories. The claims meet step 1 as follows:
Claims 1-20 recite an article of manufacture (apparatus).
Step 2A, Prong 1 (see MPEP 2106.04(I)): In this step of the analysis, judicial exception(s) that fall into one or more of the abstract idea groupings enumerated in MPEP 2106.04(a) are identified.
The claims recite the following judicial exceptions:
In claims 1, 8, 17, “collect user information associated with a user of a golf vehicle by monitoring the user as the user plays a round of golf; determine at least one golf skill level of the user; generate a user profile for the user including at least one skill level of the user; receive context information; perform an action responsive to the context information.” And in dependent claims 3 and 10, providing a recommendation to a user regarding an upcoming golf swing.
“Mental Processes” (See MPEP § 2106.04(a)(2)(III)). Observing a human being golfing, evaluating their skill level and making judgments and forming opinions on what clubs they should use is activity traditionally performed by human beings – a notoriously well-known scenario being a caddy, golf instructor or golf partner observing a golfer’s play, forming opinions on what clubs would work well in certain scenarios on the course, and making appropriate recommendations. The claims do not positively claim a novel apparatus that takes the above limitations out of the realm of possibility of human mental work. A golf vehicle is mentioned in the preamble, which does not breathe life into the claim. And although it is mentioned in the body of the claim that information is collected from “a user of a golf vehicle”, labeling information as such does not positively claim the vehicle or limit the claim in any way related to structural requirements imposed by a vehicle or to data of any particular type requiring a vehicle (such as telematics). The claims do not recite any particulars of how computers are programmed to process or analyze player inputs using a model, how a model is trained, how a model makes inferences, or how machine learning models are used to generate outputs. The claims are essentially a list of functionally-recited desired end results for a conceived field of use for machine learning. The claims do not delineate steps through which any particular ML model achieves an improvement. Support for why the pending claimed invention is directed to abstract mental processes can be found in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), wherein "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, were held to be abstract.
“Certain methods of organizing human activity,” see MPEP 2106.04(a)(2)(III)(A), which includes activities of “teaching” and “following rules or instructions”. MPEP 2106.04(a)(2)(II) notes that certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.” A user interacting with some computerized “golf vehicle system” to receive teaching instructions on club selection falls into this grouping.
identify a user of a vehicle by learning their voice or visually recognizing them (claims 13, 14);
These high-level functions attributed to some “machine learning model” of unclaimed particulars are rooted in organized human activity and mental processes; learning to recognize others based on their voices or appearance are fundamental human activities that long predate the existence of computer aids. And nothing in the claims attribute any defined nonobvious hardware or software being required for these tasks.
applying certain rules or permissions such as location-based permissions and a user requesting a suspension of these rules (claims 15-16, 19-20);
the enumerated grouping of “Certain methods of organizing human activity” includes following rules or instructions.
Step 2A, Prong 2: In this step, any additional elements beyond the identified abstract ideas are identified and evaluated for any integration into a practical application. In particular, any claimed technological improvement is considered.
Additional elements recited in the claims include:
a sensor; at least one processing circuit having memory… (claims 1, 8); a camera or a microphone used to capture image or audio data; (claims 2, 9); autonomous shot tracker that determines distance (claims 4, 12);
These hardware components are neither claimed nor disclosed as being anything other than generic devices being used for their inherent purposes (a microphone capturing audio, a camera capturing images, a shot tracker having no particular technical specifications capturing shot data including distance). The instant disclosure’s silence as to any of the claimed hardware or software having any nonobvious structural or functional specifications or requirements or being used to solve any stated problems existing in computers per se or to provide any improvements to computers per se supports a finding that the hardware additional elements of the claimed apparatus comprise merely generic components and technologies used in their routine and conventional capacities in the art. “[T]he invocation of ‘already-available computers that are not themselves plausibly asserted to be an advance … amounts to a recitation of what is well-understood, routine, and conventional.” Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1366 (Fed. Cir. 2020). And “simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” Affinity Labs v. DirectTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)
collect user information (claims 1, 8, 17); receive inputs in the form of verbal requests from a user (claims 5, 15); received user information includes image or audio data (claim 9);
“The receiving of input and storing steps represent the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” Affinity Labs v. DirectTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016 attributing particular generic computer functions for computer hardware to perform from well-known, routine, conventional functions performed by such hardware has been held to be insufficient to show an improvement to technology, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016).
a determined golf skill level of a user includes an average distance (claim 11)
This claim is directed to specific contents of data (“data per se”), in other words information that does not have a physical or tangible form, is cited in MPEP 2106.03(I) as an example of claims that are not directed to any statutory category, and as such, cannot impart eligibility to abstract ideas being applied by generic computers.
identifying a user based on user ID or received visual or verbal data collected (claims 14, 18-19);
These steps are seen as filtering data, which has been held in Bascom Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349, 119 USPQ2d 1236, to represent an abstract idea of the grouping of “certain methods of organizing human activity” wherein “[f]iltering software, apparently composed of filtering schemes and filtering elements, was well-known in the prior art” and “using ISP servers to filter content was well-known to practitioners.”
providing a user with information pertaining to a golf hole or a recommendation pertaining to the hole (claim 7); perform an action; the action includes recommending a type of golf club; (claims 3, 10); generating a food order request (claim 6);
These steps represent insignificant post-solution activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). In Flook, the Court reasoned that “[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance.”
determine, by a machine learning application, at least one golf skill level based on the user information; generate, by the machine learning application, a user profile, perform by the machine learning application, an action; (in claims 1, 8, 17); the machine learning information is configured to identify the user (claim 13)
Applying existing machine learning models to a particular field of use has been held to be ineligible by the Federal Circuit in RECENTIVE ANALYTICS, INC. v. FOX CORP., FOX BROADCASTING CO., FOX SPORTS PRODUCTIONS, LLC (2023-2437, 04/18/2025). In RECENTIVE, the two patents at issue, ‘367 and ‘960, are “Machine Learning Training” patents that are used to learn from the scheduling of live events by iteratively collecting, training, outputting, and updating a model. The outputs of the model in RECENTIVE were, in one patent, used for recommending updates to optimize a live event schedule.
The “determine,” “generate” and “perform” limitations of the pending application parallel RECENTIVE in that they concern collecting [unspecified types of golfer input data], training [training is implicit but unclaimed or else a machine learning model could not perform the steps claimed], and outputting by a model [recommending a type of golf club to use for an upcoming swing], and wherein the outputs are recommendations for optimizing [golf game play].
The Federal Circuit noted that in RECENTIVE, the specification teaches that the machine learning model “employs any suitable machine learning technique” such as a “random forest, a regression, a neural network, a decision tree” or “a Bayesian network [or] other type of technique”.
In the pending application in [0039] and [0057] of the specification, it is stated that learning models 108 may comprise GAI, LLM’s, linear regression, RAG applications, deep learning, supervised or unsupervised learning, CNN’s, LSTM “or any other suitable artificial intelligence models, frameworks, or other applications configured to perform the various functionalities” of the invention. So the pending application, like the patents in RECENTIVE, is directed to training existing machine learning models for a conceived field of use.
In step 1 of the Alice test, the Federal Circuit found that RECENTIVE claimed an activity that pre-dated the existence of machine learning – event planners considering prior ticket sales, weather forecasts, etc. to determine when and where to schedule event(s). The Federal Circuit explained that applying machine learning to such a field of use was not patent eligible, citing that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.” Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015). The court also held that “the application of existing technology to a novel database does not create patent eligibility.” and cited SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018); Elec. Power, 830 F.3d at 1353 (“[W]e have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas.”
The pending claimed activities of observing golf game play by a player and making recommendations on how to improve [through club selection] pre-dates the existence of machine learning and were traditionally conducted by human beings such as a caddy, instructor or golf partner. Limiting the use of some unclaimed type of learning model to applying activities rooted in human mental work and organized human activity is not patent eligible and furthermore collecting input data in a database does not create patent eligibility.
In Alice step two, the Federal circuit court in RECENTIVE agreed with the district court’s finding that the RECENTIVE patents were not directed to an “inventive concept” that would “amount[] to significantly more than a patent upon the [ineligible concept] itself,” id. at 456 (quoting Alice, 573 U.S. at 217–18), because the machine learning limitations were no more than “broad, functionally described, well-known techniques” and claimed “only generic and conventional computing devices,” id. at 457.
The pending claims, too, merely recite the use of a machine learning model in broad, functionally described, well-known techniques (determine, generate, perform actions) and fail to provide an inventive concept for the same reason.
The Federal Circuit held in RECENTIVE that patents that merely claim applying existing machine learning models to a new field of use are not patent eligible. The Federal Circuit agreed with the District Court that claims to machine learning training were directed to abstract ideas when they “do not delineate steps through which the machine learning technology achieves an improvement.” The Federal circuit explained that patents to machine learning training were not eligible when “they appl[ied] machine learning to [a] new field of use.” The court held “that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
The same conclusion is reached in the pending claims. The description in the claims of “a machine learning application” is limited to mere mention thereof. There is nothing about any model used in this application, any particular programming of computers using it, or any of the computer hardware claimed that reveals anything more than generic application of the model to a conceived new field of use. The claims are drafted using result-oriented language that lists desired end results of operating generic computers without describing in any detail how any of the desired end results are accomplished. There are also no details in the claims that reveal what specific types of learning model(s) are used or any details of computer programming that enables training or using the models. As such a broadest reasonable reading of the claims is that prior art existing learning models are being trained, at best, in a new field of use.
The preceding additional elements, considered alone and in the context of the claims, do not integrate the abstract game management or game rules into a practical application that improves computer functionality or another technology. They:
Invoke generic computers, memories, and conventional networked game environments. See where the instant specification discloses a general-purpose processor in [0037] and the use of any known existing learning models in [0039]. “[T]he invocation of ‘already-available computers that are not themselves plausibly asserted to be an advance … amounts to a recitation of what is well-understood, routine, and conventional.” Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1366 (Fed. Cir. 2020). And “simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” Affinity Labs v. DirectTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)
Do not recite a specific improvement to the functioning of a computer (e.g., no improved rendering pipeline, no reduced latency synchronization protocol, no novel memory management, no graphics or physics engine enhancement).
Do not effect a transformation of an article.
Are drafted as applying the abstract idea in the field of golfer data analysis (field-of-use) with result-oriented language (e.g., “collect,” “determine,” “generate,” “receive,” “perform”).
With regard to interpreting result-oriented claim language when performing a 35 USC §101 analysis, see Beteiro LLC v. DraftKings Inc., (Fed. Cir 2024) when "the claims are drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves these results. Claims of this nature are almost always found to be ineligible for patenting under Section 101." See also Interval Licensing LLC v. AOL Inc. (896 F.3d 1335) wherein the court found that claims to a computer software "attention manager" that displays content on unused portions of a screen were result-oriented and invalid under 35 U.S.C. § 101 because they did not recite a specific technological method for achieving the claimed result; Contour IP Holding LLC v. GoPro, Inc., 2024 U.S. App. LEXIS 22825 (Fed. Cir. 2024): The court held that claims must not only describe desired outcomes but also include a specific process or machinery for achieving that result; In re Killian, 45 F.4th 1373 (Fed. Cir. 2022): The court reaffirmed that claims simply reciting a desired result without specifying how to achieve it are directed to an abstract idea and are ineligible under 35 U.S.C. § 101. The claims at issue were directed to analyzing data from two databases. In the Step Two of the Alice test, the court determined that there was no inventive concept because the additional elements merely involved generic and routine data gathering and analysis steps that could have been performed with or without a computer.
MPEP § 2106.05(f) explains that, “The recitation of claim limitations that attempt to cover any solution to an identified problem with no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"”.
The pending claims do not include any technical description of mechanisms for accomplishing the claimed results. Instead, the claims use some unspecified computer and unspecified programming to leverage some unspecified machine learning application to conduct generic, result-oriented steps such as “determine”, “generate” and “perform” for performing equivalents of mental processes and organized human activities. The claims seek to cover any system and any method (such as any hardware devices, any programming instructions, any machine learning model and training method thereof) for applying the abstract ideas. As such the claims are found to be directed to ineligible subject matter.
Step 2A Prong 2 concludes in a determination that the additional elements do not amount to a practical application of the claimed abstract ideas.
Step 2B: In this step of the Alice analysis, it is assessed whether additional elements amount to significantly more than abstract ideas. Any well-understood, routine, conventional (“WURC”) activity is also discussed along with evidentiary considerations.
Absent integration into a practical application, the claims lack “significantly more” than the abstract idea.
Additional elements that are generic computer implementation and conventional components are:
“processing circuits”, “a user portal”, “a user computing device”.
The specification characterizes these computing components as being conventional computing hardware and software performing ordinary functions (spec [0037] and [0039], prior art processors being used with prior art learning models), supporting a finding that the implementation is well-understood, routine, and conventional (WURC). See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018) (WURC must be supported); here, the instant specification itself indicates conventionality.
A thorough analysis of each and every limitation of each and every claim, both individually and as part of an ordered combination shows that the claims 1-20 are not patent-eligible under 35 USC § 101.
Conclusion:
Claims 1-20 are found to be ineligible under 35 U.S.C. § 101. Although step 1 is satisfied (the claims recite manufacture/process/machine), in Step 2A Prong 1, the claims are found to recite an abstract idea—certain methods of organizing human activity and mental processes. And as found in Step 2A Prong 2, the abstract ideas are not integrated into a practical application; only ideas of outcomes and generic computer implementation are claimed. There are no technical details in the claims that reveal how any of the claimed outcomes are to be accomplished. And performing Step 2B, there is nothing “significantly more” found beyond WURC elements as evidenced by the specification.
Possible remedies:
To improve subject matter eligibility under 35 USC § 101, it is recommended to anchor the claims to concrete, non-generic technical mechanisms (such as particular software processes or nonobvious system architectures) in a way that there is evidence in the claims of certain improvements to computer or network operations or to another technology. In the field of the instant invention (golf game management using computers), an improvement would have to be found to an inherently technical problem existing in computers and would have to reveal how the computer(s) themselves are improved as a direct result of the claimed invention. The details of the improvement to computers cannot be found in the wording of the abstract ideas (details of golfer skill evaluation capable of being performed by human beings) themselves. Genetic Techs v Merial, an inventive concept "cannot be furnished by the unpatentable law of nature" itself. A subjective improvement in a user’s experience (by providing certain results of data analysis accomplished by unspecified hardware or software) is not an improvement to computers themselves or to computer technology and does not solve any stated problem that is inherently technical in nature.The court ruled in International Business Machines Corporation v. Zillow Group, Inc., (CAFC, 17 October, 2022), that "improving a user's experience while using a computer application is not, without more, sufficient to render the claims" patent-eligible. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020).
Examples might include to:
Tie abstract steps to a specific, non-generic technological implementation that improves computer functionality or another technology (e.g., reduces network latency by X, improves memory utilization via Y, improves image fidelity through Z), with technical mechanisms claimed.
Provide evidence of improvements to computers or network operations in the claims by claiming certain network nonobvious server-side architecture that is also claimed as solving problems existing in the art, or claiming a certain improvement in rendering such as a GPU-accelerated improvement that provides measurable improvements to game functionality.
Add claim elements showing a particular machine or a transformation of an article, beyond mere data manipulation or display functions.
Replace result-oriented terms (“determine, by a machine learning application…”, “generate, by a machine learning application”, “perform, by the machine learning application”) with concrete steps and parameters tied to the technical mechanism (e.g., explicit algorithmic operations, message formats).
Limit scope to a specific technological field and architecture (e.g., “a distributed game server cluster employing [named protocol] with defined message cadence and buffer management”) and claim the architecture itself, avoiding broad “apply it on a computer” formulations.
Provide specification support demonstrating the asserted improvements are not well-understood, routine and conventional:
Implementation details: algorithms with stepwise operations, data structures with constraints, hardware configurations, protocol diagrams.
Performance evidence: benchmarks, latency/throughput graphs, memory usage comparisons versus baselines.
Engineering rationale: why existing approaches fail and how your mechanism achieves measurable gains.
Recite in the claims a technical solution to a technological problem (e.g., secure hardware-backed attestations, novel protocol flows, improved cryptographic operations, sensor fusion pipelines).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN J HYLINSKI whose telephone number is (571)270-1995. The examiner can normally be reached Mon-Fri 10-530.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dmitry Suhol can be reached at (571) 272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/STEVEN J HYLINSKI/Primary Examiner, Art Unit 3715
Prosecution Insights